Overview

Dataset statistics

Number of variables20
Number of observations186123
Missing cells108933
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.7 MiB
Average record size in memory111.0 B

Variable types

Categorical4
Text3
Numeric9
Boolean4

Alerts

firing_type has a high cardinality: 107 distinct valuesHigh cardinality
firing_type is highly imbalanced (66.4%)Imbalance
flat_type has 25049 (13.5%) missing valuesMissing
telekom_uploadspeed has 22095 (11.9%) missing valuesMissing
firing_type has 35948 (19.3%) missing valuesMissing
heating_type has 25841 (13.9%) missing valuesMissing
immoscout_id has unique valuesUnique
floor has 20974 (11.3%) zerosZeros
service_charge has 2265 (1.2%) zerosZeros

Reproduction

Analysis started2024-04-15 20:21:42.498209
Analysis finished2024-04-15 20:21:56.255641
Duration13.76 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

bundesland
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Sachsen
43498 
Nordrhein_Westfalen
42173 
Sachsen_Anhalt
15816 
Bayern
14492 
Hessen
11277 
Other values (11)
58867 

Length

Max length22
Median length18
Mean length12.091708
Min length6

Characters and Unicode

Total characters2250545
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNordrhein_Westfalen
2nd rowSachsen
3rd rowBremen
4th rowSachsen
5th rowBaden_Württemberg

Common Values

ValueCountFrequency (%)
Sachsen 43498
23.4%
Nordrhein_Westfalen 42173
22.7%
Sachsen_Anhalt 15816
 
8.5%
Bayern 14492
 
7.8%
Hessen 11277
 
6.1%
Baden_Württemberg 10019
 
5.4%
Niedersachsen 9122
 
4.9%
Berlin 8638
 
4.6%
Thüringen 5987
 
3.2%
Brandenburg 5578
 
3.0%
Other values (6) 19523
10.5%

Length

2024-04-15T22:21:56.372629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sachsen 43498
23.4%
nordrhein_westfalen 42173
22.7%
sachsen_anhalt 15816
 
8.5%
bayern 14492
 
7.8%
hessen 11277
 
6.1%
baden_wã¼rttemberg 10019
 
5.4%
niedersachsen 9122
 
4.9%
berlin 8638
 
4.6%
thã¼ringen 5987
 
3.2%
brandenburg 5578
 
3.0%
Other values (6) 19523
10.5%

Most occurring characters

ValueCountFrequency (%)
e 333346
14.8%
n 262916
 
11.7%
r 174158
 
7.7%
a 170619
 
7.6%
s 151041
 
6.7%
h 141574
 
6.3%
l 90621
 
4.0%
t 82405
 
3.7%
_ 82179
 
3.7%
i 79460
 
3.5%
Other values (26) 682226
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1868052
83.0%
Uppercase Letter 284308
 
12.6%
Connector Punctuation 82179
 
3.7%
Other Number 16006
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 333346
17.8%
n 262916
14.1%
r 174158
9.3%
a 170619
9.1%
s 151041
8.1%
h 141574
7.6%
l 90621
 
4.9%
t 82405
 
4.4%
i 79460
 
4.3%
c 77823
 
4.2%
Other values (12) 304089
16.3%
Uppercase Letter
ValueCountFrequency (%)
S 64353
22.6%
W 52192
18.4%
N 51295
18.0%
B 40864
14.4%
H 18209
 
6.4%
à 16006
 
5.6%
A 15816
 
5.6%
T 5987
 
2.1%
M 5009
 
1.8%
V 5009
 
1.8%
Other values (2) 9568
 
3.4%
Connector Punctuation
ValueCountFrequency (%)
_ 82179
100.0%
Other Number
ValueCountFrequency (%)
¼ 16006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2152360
95.6%
Common 98185
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 333346
15.5%
n 262916
12.2%
r 174158
 
8.1%
a 170619
 
7.9%
s 151041
 
7.0%
h 141574
 
6.6%
l 90621
 
4.2%
t 82405
 
3.8%
i 79460
 
3.7%
c 77823
 
3.6%
Other values (24) 588397
27.3%
Common
ValueCountFrequency (%)
_ 82179
83.7%
¼ 16006
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2218533
98.6%
None 32012
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 333346
15.0%
n 262916
11.9%
r 174158
 
7.9%
a 170619
 
7.7%
s 151041
 
6.8%
h 141574
 
6.4%
l 90621
 
4.1%
t 82405
 
3.7%
_ 82179
 
3.7%
i 79460
 
3.6%
Other values (24) 650214
29.3%
None
ValueCountFrequency (%)
à 16006
50.0%
¼ 16006
50.0%

city
Text

Distinct419
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-04-15T22:21:56.529422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length39
Median length28
Mean length11.713727
Min length3

Characters and Unicode

Total characters2180194
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDortmund
2nd rowDresden
3rd rowBremen
4th rowMittelsachsen_Kreis
5th rowEmmendingen_Kreis
ValueCountFrequency (%)
leipzig 10808
 
5.8%
chemnitz 9335
 
5.0%
berlin 8638
 
4.6%
dresden 5373
 
2.9%
magdeburg 4212
 
2.3%
halle_saale 3585
 
1.9%
mã¼nchen 3344
 
1.8%
essen 2993
 
1.6%
frankfurt_am_main 2889
 
1.6%
dã¼sseldorf 2792
 
1.5%
Other values (409) 132154
71.0%
2024-04-15T22:21:56.847138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 287831
 
13.2%
r 200632
 
9.2%
i 185861
 
8.5%
s 150386
 
6.9%
n 142913
 
6.6%
a 109890
 
5.0%
_ 108756
 
5.0%
K 74921
 
3.4%
l 74013
 
3.4%
g 66504
 
3.1%
Other values (44) 778487
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1724090
79.1%
Uppercase Letter 321952
 
14.8%
Connector Punctuation 108756
 
5.0%
Other Number 14395
 
0.7%
Other Punctuation 7411
 
0.3%
Currency Symbol 3590
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 74921
23.3%
à 29673
 
9.2%
M 25188
 
7.8%
B 23096
 
7.2%
S 20129
 
6.3%
L 18570
 
5.8%
H 17129
 
5.3%
D 16953
 
5.3%
R 12794
 
4.0%
C 11060
 
3.4%
Other values (15) 72439
22.5%
Lowercase Letter
ValueCountFrequency (%)
e 287831
16.7%
r 200632
11.6%
i 185861
10.8%
s 150386
 
8.7%
n 142913
 
8.3%
a 109890
 
6.4%
l 74013
 
4.3%
g 66504
 
3.9%
t 64357
 
3.7%
u 63822
 
3.7%
Other values (14) 377881
21.9%
Other Punctuation
ValueCountFrequency (%)
¶ 7337
99.0%
. 74
 
1.0%
Connector Punctuation
ValueCountFrequency (%)
_ 108756
100.0%
Other Number
ValueCountFrequency (%)
¼ 14395
100.0%
Currency Symbol
ValueCountFrequency (%)
¤ 3590
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2046042
93.8%
Common 134152
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 287831
14.1%
r 200632
 
9.8%
i 185861
 
9.1%
s 150386
 
7.4%
n 142913
 
7.0%
a 109890
 
5.4%
K 74921
 
3.7%
l 74013
 
3.6%
g 66504
 
3.3%
t 64357
 
3.1%
Other values (39) 688734
33.7%
Common
ValueCountFrequency (%)
_ 108756
81.1%
¼ 14395
 
10.7%
¶ 7337
 
5.5%
¤ 3590
 
2.7%
. 74
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2120848
97.3%
None 59346
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 287831
13.6%
r 200632
 
9.5%
i 185861
 
8.8%
s 150386
 
7.1%
n 142913
 
6.7%
a 109890
 
5.2%
_ 108756
 
5.1%
K 74921
 
3.5%
l 74013
 
3.5%
g 66504
 
3.1%
Other values (39) 719141
33.9%
None
ValueCountFrequency (%)
à 29673
50.0%
¼ 14395
24.3%
¶ 7337
 
12.4%
Ÿ 4351
 
7.3%
¤ 3590
 
6.0%
Distinct7635
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-04-15T22:21:57.031336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length49
Median length38
Mean length11.374505
Min length2

Characters and Unicode

Total characters2117057
Distinct characters70
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1582 ?
Unique (%)0.8%

Sample

1st rowSchüren
2nd rowÄußere_Neustadt_Antonstadt
3rd rowNeu_Schwachhausen
4th rowFreiberg
5th rowDenzlingen
ValueCountFrequency (%)
innenstadt 3132
 
1.7%
stadtmitte 1972
 
1.1%
altstadt 1776
 
1.0%
sonnenberg 1499
 
0.8%
kaãÿberg 1317
 
0.7%
mitte 1071
 
0.6%
schloãÿchemnitz 993
 
0.5%
hilbersdorf 977
 
0.5%
sã¼dstadt 857
 
0.5%
zentrum 797
 
0.4%
Other values (7625) 171732
92.3%
2024-04-15T22:21:57.372646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 267900
 
12.7%
n 155124
 
7.3%
r 153922
 
7.3%
t 151118
 
7.1%
a 122662
 
5.8%
i 101955
 
4.8%
d 97043
 
4.6%
s 96948
 
4.6%
l 86341
 
4.1%
h 81361
 
3.8%
Other values (60) 802683
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1718070
81.2%
Uppercase Letter 295621
 
14.0%
Connector Punctuation 61592
 
2.9%
Other Punctuation 20538
 
1.0%
Other Number 18435
 
0.9%
Currency Symbol 2205
 
0.1%
Open Punctuation 272
 
< 0.1%
Decimal Number 172
 
< 0.1%
Dash Punctuation 152
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
à 42256
14.3%
S 33300
 
11.3%
B 21721
 
7.3%
N 18508
 
6.3%
H 16830
 
5.7%
W 16412
 
5.6%
M 15909
 
5.4%
L 13253
 
4.5%
G 12381
 
4.2%
A 10854
 
3.7%
Other values (18) 94197
31.9%
Lowercase Letter
ValueCountFrequency (%)
e 267900
15.6%
n 155124
 
9.0%
r 153922
 
9.0%
t 151118
 
8.8%
a 122662
 
7.1%
i 101955
 
5.9%
d 97043
 
5.6%
s 96948
 
5.6%
l 86341
 
5.0%
h 81361
 
4.7%
Other values (17) 403696
23.5%
Other Punctuation
ValueCountFrequency (%)
¶ 13493
65.7%
/ 5476
26.7%
. 949
 
4.6%
& 411
 
2.0%
, 209
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 59
34.3%
5 42
24.4%
4 35
20.3%
2 18
 
10.5%
3 18
 
10.5%
Connector Punctuation
ValueCountFrequency (%)
_ 61592
100.0%
Other Number
ValueCountFrequency (%)
¼ 18435
100.0%
Currency Symbol
ValueCountFrequency (%)
¤ 2205
100.0%
Open Punctuation
ValueCountFrequency (%)
„ 272
100.0%
Dash Punctuation
ValueCountFrequency (%)
– 152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2013691
95.1%
Common 103366
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 267900
 
13.3%
n 155124
 
7.7%
r 153922
 
7.6%
t 151118
 
7.5%
a 122662
 
6.1%
i 101955
 
5.1%
d 97043
 
4.8%
s 96948
 
4.8%
l 86341
 
4.3%
h 81361
 
4.0%
Other values (45) 699317
34.7%
Common
ValueCountFrequency (%)
_ 61592
59.6%
¼ 18435
 
17.8%
¶ 13493
 
13.1%
/ 5476
 
5.3%
¤ 2205
 
2.1%
. 949
 
0.9%
& 411
 
0.4%
„ 272
 
0.3%
, 209
 
0.2%
– 152
 
0.1%
Other values (5) 172
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2032545
96.0%
None 84088
 
4.0%
Punctuation 424
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 267900
 
13.2%
n 155124
 
7.6%
r 153922
 
7.6%
t 151118
 
7.4%
a 122662
 
6.0%
i 101955
 
5.0%
d 97043
 
4.8%
s 96948
 
4.8%
l 86341
 
4.2%
h 81361
 
4.0%
Other values (52) 718171
35.3%
None
ValueCountFrequency (%)
à 42256
50.3%
¼ 18435
21.9%
¶ 13493
 
16.0%
Ÿ 7447
 
8.9%
¤ 2205
 
2.6%
Å“ 252
 
0.3%
Punctuation
ValueCountFrequency (%)
„ 272
64.2%
– 152
35.8%

street
Text

Distinct42737
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
2024-04-15T22:21:57.586638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length89
Median length54
Mean length16.861871
Min length1

Characters and Unicode

Total characters3138382
Distinct characters85
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24774 ?
Unique (%)13.3%

Sample

1st rowSch&uuml;ruferstra&szlig;e
2nd rowTurnerweg
3rd rowHermann-Henrich-Meier-Allee
4th rowAm Bahnhof
5th rowno_information
ValueCountFrequency (%)
no_information 37835
 
16.0%
stra&szlig;e 16053
 
6.8%
str 10541
 
4.4%
am 4788
 
2.0%
der 1952
 
0.8%
weg 1810
 
0.8%
an 1177
 
0.5%
im 925
 
0.4%
strasse 804
 
0.3%
hauptstra&szlig;e 760
 
0.3%
Other values (37522) 160232
67.6%
2024-04-15T22:21:57.948398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 290379
 
9.3%
e 280415
 
8.9%
i 219963
 
7.0%
n 215897
 
6.9%
t 212531
 
6.8%
s 197042
 
6.3%
a 194188
 
6.2%
o 164524
 
5.2%
l 156832
 
5.0%
g 112664
 
3.6%
Other values (75) 1093947
34.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2546427
81.1%
Uppercase Letter 239427
 
7.6%
Other Punctuation 216656
 
6.9%
Space Separator 50826
 
1.6%
Dash Punctuation 45873
 
1.5%
Connector Punctuation 37863
 
1.2%
Decimal Number 1220
 
< 0.1%
Close Punctuation 41
 
< 0.1%
Open Punctuation 41
 
< 0.1%
Math Symbol 2
 
< 0.1%
Other values (5) 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 290379
11.4%
e 280415
11.0%
i 219963
8.6%
n 215897
8.5%
t 212531
8.3%
s 197042
 
7.7%
a 194188
 
7.6%
o 164524
 
6.5%
l 156832
 
6.2%
g 112664
 
4.4%
Other values (18) 501992
19.7%
Uppercase Letter
ValueCountFrequency (%)
S 64317
26.9%
B 16623
 
6.9%
H 16393
 
6.8%
A 15868
 
6.6%
W 12744
 
5.3%
K 12639
 
5.3%
L 10942
 
4.6%
M 10572
 
4.4%
R 10545
 
4.4%
G 10166
 
4.2%
Other values (17) 58618
24.5%
Decimal Number
ValueCountFrequency (%)
1 274
22.5%
5 161
13.2%
4 147
12.0%
2 128
10.5%
3 122
10.0%
9 84
 
6.9%
6 82
 
6.7%
8 81
 
6.6%
7 79
 
6.5%
0 62
 
5.1%
Other Punctuation
ValueCountFrequency (%)
; 87572
40.4%
& 87569
40.4%
. 41267
19.0%
, 125
 
0.1%
/ 115
 
0.1%
: 3
 
< 0.1%
¿ 2
 
< 0.1%
* 2
 
< 0.1%
? 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
50826
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45873
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 37863
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
€ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2785855
88.8%
Common 352527
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 290379
 
10.4%
e 280415
 
10.1%
i 219963
 
7.9%
n 215897
 
7.7%
t 212531
 
7.6%
s 197042
 
7.1%
a 194188
 
7.0%
o 164524
 
5.9%
l 156832
 
5.6%
g 112664
 
4.0%
Other values (46) 741420
26.6%
Common
ValueCountFrequency (%)
; 87572
24.8%
& 87569
24.8%
50826
14.4%
- 45873
13.0%
. 41267
11.7%
_ 37863
10.7%
1 274
 
0.1%
5 161
 
< 0.1%
4 147
 
< 0.1%
2 128
 
< 0.1%
Other values (19) 847
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3138371
> 99.9%
None 9
 
< 0.1%
Currency Symbols 1
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 290379
 
9.3%
e 280415
 
8.9%
i 219963
 
7.0%
n 215897
 
6.9%
t 212531
 
6.8%
s 197042
 
6.3%
a 194188
 
6.2%
o 164524
 
5.2%
l 156832
 
5.0%
g 112664
 
3.6%
Other values (67) 1093936
34.9%
None
ValueCountFrequency (%)
¿ 2
22.2%
ï 2
22.2%
½ 2
22.2%
Ã… 1
11.1%
â 1
11.1%
ª 1
11.1%
Currency Symbols
ValueCountFrequency (%)
€ 1
100.0%
Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

zip_code
Real number (ℝ)

Distinct6884
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35642.157
Minimum852
Maximum99994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-04-15T22:21:58.102195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum852
5-th percentile2625
Q19113
median35796
Q353844
95-th percentile87700
Maximum99994
Range99142
Interquartile range (IQR)44731

Descriptive statistics

Standard deviation27816.579
Coefficient of variation (CV)0.7804404
Kurtosis-0.81335796
Mean35642.157
Median Absolute Deviation (MAD)25552
Skewness0.50985073
Sum6.6338252 × 109
Variance7.7376208 × 108
MonotonicityNot monotonic
2024-04-15T22:21:58.247409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9130 1558
 
0.8%
9126 1518
 
0.8%
9112 1244
 
0.7%
9131 1209
 
0.6%
9113 1107
 
0.6%
8056 836
 
0.4%
39112 774
 
0.4%
6217 764
 
0.4%
4157 761
 
0.4%
39108 720
 
0.4%
Other values (6874) 175632
94.4%
ValueCountFrequency (%)
852 1
 
< 0.1%
853 1
 
< 0.1%
1057 3
 
< 0.1%
1067 566
0.3%
1069 158
 
0.1%
1097 310
0.2%
1099 368
0.2%
1108 27
 
< 0.1%
1109 64
 
< 0.1%
1127 152
 
0.1%
ValueCountFrequency (%)
99994 6
 
< 0.1%
99991 5
 
< 0.1%
99976 4
 
< 0.1%
99974 120
0.1%
99958 1
 
< 0.1%
99955 3
 
< 0.1%
99947 68
< 0.1%
99898 1
 
< 0.1%
99897 1
 
< 0.1%
99894 4
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
False
122177 
True
63946 
ValueCountFrequency (%)
False 122177
65.6%
True 63946
34.4%
2024-04-15T22:21:58.370171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

balcony
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
True
117961 
False
68162 
ValueCountFrequency (%)
True 117961
63.4%
False 68162
36.6%
2024-04-15T22:21:58.463817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

lift
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
False
140148 
True
45975 
ValueCountFrequency (%)
False 140148
75.3%
True 45975
 
24.7%
2024-04-15T22:21:58.556757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

garden
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
False
149127 
True
36996 
ValueCountFrequency (%)
False 149127
80.1%
True 36996
 
19.9%
2024-04-15T22:21:58.651776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

floor
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1089065
Minimum-1
Maximum45
Zeros20974
Zeros (%)11.3%
Negative259
Negative (%)0.1%
Memory size2.8 MiB
2024-04-15T22:21:58.758533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum45
Range46
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.669126
Coefficient of variation (CV)0.79146516
Kurtosis18.089261
Mean2.1089065
Median Absolute Deviation (MAD)1
Skewness2.4111115
Sum392516
Variance2.7859816
MonotonicityNot monotonic
2024-04-15T22:21:58.877776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 54343
29.2%
2 48763
26.2%
3 32591
17.5%
0 20974
 
11.3%
4 17395
 
9.3%
5 6917
 
3.7%
6 2079
 
1.1%
7 894
 
0.5%
8 502
 
0.3%
9 371
 
0.2%
Other values (24) 1294
 
0.7%
ValueCountFrequency (%)
-1 259
 
0.1%
0 20974
 
11.3%
1 54343
29.2%
2 48763
26.2%
3 32591
17.5%
4 17395
 
9.3%
5 6917
 
3.7%
6 2079
 
1.1%
7 894
 
0.5%
8 502
 
0.3%
ValueCountFrequency (%)
45 1
 
< 0.1%
41 1
 
< 0.1%
36 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
29 2
< 0.1%
26 2
< 0.1%
25 1
 
< 0.1%
24 3
< 0.1%
23 1
 
< 0.1%

flat_type
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing25049
Missing (%)13.5%
Memory size1.6 MiB
apartment
99518 
roof_storey
23694 
ground_floor
15942 
other
 
6532
maisonette
 
6253
Other values (5)
 
9135

Length

Max length19
Median length9
Mean length9.7160808
Min length4

Characters and Unicode

Total characters1565008
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowground_floor
2nd rowapartment
3rd rowapartment
4th rowroof_storey
5th rowapartment

Common Values

ValueCountFrequency (%)
apartment 99518
53.5%
roof_storey 23694
 
12.7%
ground_floor 15942
 
8.6%
other 6532
 
3.5%
maisonette 6253
 
3.4%
raised_ground_floor 3128
 
1.7%
penthouse 2420
 
1.3%
terraced_flat 2228
 
1.2%
half_basement 734
 
0.4%
loft 625
 
0.3%
(Missing) 25049
 
13.5%

Length

2024-04-15T22:21:59.004793image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-15T22:21:59.142814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
apartment 99518
61.8%
roof_storey 23694
 
14.7%
ground_floor 15942
 
9.9%
other 6532
 
4.1%
maisonette 6253
 
3.9%
raised_ground_floor 3128
 
1.9%
penthouse 2420
 
1.5%
terraced_flat 2228
 
1.4%
half_basement 734
 
0.5%
loft 625
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 250003
16.0%
a 214341
13.7%
r 199162
12.7%
e 156142
10.0%
o 144122
9.2%
n 127995
8.2%
m 106505
6.8%
p 101938
6.5%
_ 48854
 
3.1%
f 46351
 
3.0%
Other values (10) 169595
10.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1516154
96.9%
Connector Punctuation 48854
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 250003
16.5%
a 214341
14.1%
r 199162
13.1%
e 156142
10.3%
o 144122
9.5%
n 127995
8.4%
m 106505
7.0%
p 101938
6.7%
f 46351
 
3.1%
s 36229
 
2.4%
Other values (9) 133366
8.8%
Connector Punctuation
ValueCountFrequency (%)
_ 48854
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1516154
96.9%
Common 48854
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 250003
16.5%
a 214341
14.1%
r 199162
13.1%
e 156142
10.3%
o 144122
9.5%
n 127995
8.4%
m 106505
7.0%
p 101938
6.7%
f 46351
 
3.1%
s 36229
 
2.4%
Other values (9) 133366
8.8%
Common
ValueCountFrequency (%)
_ 48854
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1565008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 250003
16.0%
a 214341
13.7%
r 199162
12.7%
e 156142
10.0%
o 144122
9.2%
n 127995
8.2%
m 106505
6.8%
p 101938
6.5%
_ 48854
 
3.1%
f 46351
 
3.0%
Other values (10) 169595
10.8%

telekom_uploadspeed
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing22095
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean29.01464
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-04-15T22:21:59.282739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q110
median40
Q340
95-th percentile40
Maximum100
Range99
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.263861
Coefficient of variation (CV)0.56053982
Kurtosis-1.0411583
Mean29.01464
Median Absolute Deviation (MAD)0
Skewness-0.75085262
Sum4759213.4
Variance264.51318
MonotonicityNot monotonic
2024-04-15T22:21:59.538433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
40 111157
59.7%
2.4 29121
 
15.6%
10 22774
 
12.2%
5 689
 
0.4%
1 142
 
0.1%
100 116
 
0.1%
4 29
 
< 0.1%
(Missing) 22095
 
11.9%
ValueCountFrequency (%)
1 142
 
0.1%
2.4 29121
 
15.6%
4 29
 
< 0.1%
5 689
 
0.4%
10 22774
 
12.2%
40 111157
59.7%
100 116
 
0.1%
ValueCountFrequency (%)
100 116
 
0.1%
40 111157
59.7%
10 22774
 
12.2%
5 689
 
0.4%
4 29
 
< 0.1%
2.4 29121
 
15.6%
1 142
 
0.1%

firing_type
Categorical

HIGH CARDINALITY  IMBALANCE  MISSING 

Distinct107
Distinct (%)0.1%
Missing35948
Missing (%)19.3%
Memory size1.6 MiB
gas
76209 
district_heating
38253 
oil
11989 
natural_gas_light
7811 
electricity
 
3179
Other values (102)
12734 

Length

Max length187
Median length3
Mean length8.4514799
Min length3

Characters and Unicode

Total characters1269201
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)< 0.1%

Sample

1st rowoil
2nd rowgas
3rd rowoil
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 76209
40.9%
district_heating 38253
20.6%
oil 11989
 
6.4%
natural_gas_light 7811
 
4.2%
electricity 3179
 
1.7%
natural_gas_heavy 2934
 
1.6%
geothermal 1662
 
0.9%
pellet_heating 1660
 
0.9%
gas:electricity 906
 
0.5%
combined_heat_and_power_fossil_fuels 755
 
0.4%
Other values (97) 4817
 
2.6%
(Missing) 35948
19.3%

Length

2024-04-15T22:21:59.688009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas 76209
50.7%
district_heating 38253
25.5%
oil 11989
 
8.0%
natural_gas_light 7811
 
5.2%
electricity 3179
 
2.1%
natural_gas_heavy 2934
 
2.0%
geothermal 1662
 
1.1%
pellet_heating 1660
 
1.1%
gas:electricity 906
 
0.6%
combined_heat_and_power_fossil_fuels 755
 
0.5%
Other values (97) 4817
 
3.2%

Most occurring characters

ValueCountFrequency (%)
a 163887
12.9%
t 156553
12.3%
i 154530
12.2%
g 142185
11.2%
s 132613
10.4%
_ 72388
 
5.7%
e 71154
 
5.6%
r 60936
 
4.8%
n 58650
 
4.6%
h 57430
 
4.5%
Other values (15) 198875
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1194552
94.1%
Connector Punctuation 72388
 
5.7%
Other Punctuation 2261
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 163887
13.7%
t 156553
13.1%
i 154530
12.9%
g 142185
11.9%
s 132613
11.1%
e 71154
6.0%
r 60936
 
5.1%
n 58650
 
4.9%
h 57430
 
4.8%
c 50734
 
4.2%
Other values (13) 145880
12.2%
Connector Punctuation
ValueCountFrequency (%)
_ 72388
100.0%
Other Punctuation
ValueCountFrequency (%)
: 2261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1194552
94.1%
Common 74649
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 163887
13.7%
t 156553
13.1%
i 154530
12.9%
g 142185
11.9%
s 132613
11.1%
e 71154
6.0%
r 60936
 
5.1%
n 58650
 
4.9%
h 57430
 
4.8%
c 50734
 
4.2%
Other values (13) 145880
12.2%
Common
ValueCountFrequency (%)
_ 72388
97.0%
: 2261
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1269201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 163887
12.9%
t 156553
12.3%
i 154530
12.2%
g 142185
11.2%
s 132613
10.4%
_ 72388
 
5.7%
e 71154
 
5.6%
r 60936
 
4.8%
n 58650
 
4.6%
h 57430
 
4.5%
Other values (15) 198875
15.7%

heating_type
Categorical

MISSING 

Distinct13
Distinct (%)< 0.1%
Missing25841
Missing (%)13.9%
Memory size1.6 MiB
central_heating
91009 
district_heating
19280 
gas_heating
15171 
self_contained_central_heating
12671 
floor_heating
12513 
Other values (8)
9638 

Length

Max length30
Median length15
Mean length15.802511
Min length9

Characters and Unicode

Total characters2532858
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcentral_heating
2nd rowfloor_heating
3rd rowself_contained_central_heating
4th rowself_contained_central_heating
5th rowoil_heating

Common Values

ValueCountFrequency (%)
central_heating 91009
48.9%
district_heating 19280
 
10.4%
gas_heating 15171
 
8.2%
self_contained_central_heating 12671
 
6.8%
floor_heating 12513
 
6.7%
oil_heating 3619
 
1.9%
heat_pump 1760
 
0.9%
combined_heat_and_power_plant 1580
 
0.8%
night_storage_heater 1018
 
0.5%
wood_pellet_heating 717
 
0.4%
Other values (3) 944
 
0.5%
(Missing) 25841
 
13.9%

Length

2024-04-15T22:21:59.818158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
central_heating 91009
56.8%
district_heating 19280
 
12.0%
gas_heating 15171
 
9.5%
self_contained_central_heating 12671
 
7.9%
floor_heating 12513
 
7.8%
oil_heating 3619
 
2.3%
heat_pump 1760
 
1.1%
combined_heat_and_power_plant 1580
 
1.0%
night_storage_heater 1018
 
0.6%
wood_pellet_heating 717
 
0.4%
Other values (3) 944
 
0.6%

Most occurring characters

ValueCountFrequency (%)
t 320339
12.6%
e 297360
11.7%
a 296113
11.7%
n 290704
11.5%
i 213985
8.4%
_ 192099
7.6%
g 173131
6.8%
h 161300
6.4%
r 139833
5.5%
c 138437
5.5%
Other values (11) 309557
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2340759
92.4%
Connector Punctuation 192099
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 320339
13.7%
e 297360
12.7%
a 296113
12.7%
n 290704
12.4%
i 213985
9.1%
g 173131
7.4%
h 161300
6.9%
r 139833
6.0%
c 138437
5.9%
l 136241
5.8%
Other values (10) 173316
7.4%
Connector Punctuation
ValueCountFrequency (%)
_ 192099
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2340759
92.4%
Common 192099
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 320339
13.7%
e 297360
12.7%
a 296113
12.7%
n 290704
12.4%
i 213985
9.1%
g 173131
7.4%
h 161300
6.9%
r 139833
6.0%
c 138437
5.9%
l 136241
5.8%
Other values (10) 173316
7.4%
Common
ValueCountFrequency (%)
_ 192099
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2532858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 320339
12.6%
e 297360
11.7%
a 296113
11.7%
n 290704
11.5%
i 213985
8.4%
_ 192099
7.6%
g 173131
6.8%
h 161300
6.4%
r 139833
5.5%
c 138437
5.5%
Other values (11) 309557
12.2%

number_of_rooms
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6626693
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-15T22:21:59.910257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum16
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.98469852
Coefficient of variation (CV)0.3698163
Kurtosis1.7996565
Mean2.6626693
Median Absolute Deviation (MAD)1
Skewness0.61704796
Sum495584
Variance0.96963117
MonotonicityNot monotonic
2024-04-15T22:22:00.018963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3 70795
38.0%
2 65207
35.0%
4 25144
 
13.5%
1 18644
 
10.0%
5 4974
 
2.7%
6 1018
 
0.5%
7 225
 
0.1%
8 79
 
< 0.1%
9 20
 
< 0.1%
10 6
 
< 0.1%
Other values (5) 11
 
< 0.1%
ValueCountFrequency (%)
1 18644
 
10.0%
2 65207
35.0%
3 70795
38.0%
4 25144
 
13.5%
5 4974
 
2.7%
6 1018
 
0.5%
7 225
 
0.1%
8 79
 
< 0.1%
9 20
 
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 2
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 4
 
< 0.1%
10 6
 
< 0.1%
9 20
 
< 0.1%
8 79
 
< 0.1%
7 225
 
0.1%
6 1018
0.5%

square_meter
Real number (ℝ)

Distinct329
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.505037
Minimum0
Maximum649
Zeros35
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-15T22:22:00.154175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q154
median67
Q385
95-th percentile129
Maximum649
Range649
Interquartile range (IQR)31

Descriptive statistics

Standard deviation30.445359
Coefficient of variation (CV)0.41990682
Kurtosis7.8370541
Mean72.505037
Median Absolute Deviation (MAD)15
Skewness1.7672785
Sum13494855
Variance926.9199
MonotonicityNot monotonic
2024-04-15T22:22:00.292007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 5925
 
3.2%
70 4498
 
2.4%
65 4240
 
2.3%
58 3882
 
2.1%
57 3786
 
2.0%
50 3778
 
2.0%
61 3747
 
2.0%
55 3612
 
1.9%
59 3573
 
1.9%
80 3525
 
1.9%
Other values (319) 145557
78.2%
ValueCountFrequency (%)
0 35
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
8 5
 
< 0.1%
9 10
 
< 0.1%
10 35
< 0.1%
11 35
< 0.1%
12 83
< 0.1%
13 54
< 0.1%
14 64
< 0.1%
ValueCountFrequency (%)
649 1
< 0.1%
527 1
< 0.1%
482 1
< 0.1%
480 2
< 0.1%
446 1
< 0.1%
430 1
< 0.1%
423 1
< 0.1%
420 2
< 0.1%
413 1
< 0.1%
400 2
< 0.1%

base_rent
Real number (ℝ)

Distinct2646
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean634.75968
Minimum0
Maximum8700
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-04-15T22:22:00.431130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile229
Q1330
median480
Q3785
95-th percentile1500
Maximum8700
Range8700
Interquartile range (IQR)455

Descriptive statistics

Standard deviation479.01583
Coefficient of variation (CV)0.75464124
Kurtosis18.999186
Mean634.75968
Median Absolute Deviation (MAD)181
Skewness3.1503837
Sum1.1814338 × 108
Variance229456.17
MonotonicityNot monotonic
2024-04-15T22:22:00.627352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350 2620
 
1.4%
450 2362
 
1.3%
300 2290
 
1.2%
400 2121
 
1.1%
650 1887
 
1.0%
550 1820
 
1.0%
320 1777
 
1.0%
500 1684
 
0.9%
750 1667
 
0.9%
330 1652
 
0.9%
Other values (2636) 166243
89.3%
ValueCountFrequency (%)
0 17
< 0.1%
1 1
 
< 0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
10 1
 
< 0.1%
11 2
 
< 0.1%
42 1
 
< 0.1%
50 1
 
< 0.1%
ValueCountFrequency (%)
8700 1
< 0.1%
8500 1
< 0.1%
8400 1
< 0.1%
7850 1
< 0.1%
7830 1
< 0.1%
7600 1
< 0.1%
7200 1
< 0.1%
7020 1
< 0.1%
7000 2
< 0.1%
6990 2
< 0.1%

total_rent
Real number (ℝ)

Distinct3213
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean805.60146
Minimum0
Maximum9000
Zeros214
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-04-15T22:22:00.776264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile325
Q1465
median640
Q3975
95-th percentile1785
Maximum9000
Range9000
Interquartile range (IQR)510

Descriptive statistics

Standard deviation538.97373
Coefficient of variation (CV)0.66903272
Kurtosis18.082371
Mean805.60146
Median Absolute Deviation (MAD)212
Skewness3.0563415
Sum1.4994096 × 108
Variance290492.69
MonotonicityNot monotonic
2024-04-15T22:22:00.912043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450 1556
 
0.8%
500 1536
 
0.8%
600 1427
 
0.8%
550 1340
 
0.7%
400 1244
 
0.7%
490 1220
 
0.7%
750 1180
 
0.6%
480 1175
 
0.6%
470 1166
 
0.6%
420 1164
 
0.6%
Other values (3203) 173115
93.0%
ValueCountFrequency (%)
0 214
0.1%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 1
 
< 0.1%
4 5
 
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
20 1
 
< 0.1%
50 2
 
< 0.1%
ValueCountFrequency (%)
9000 1
< 0.1%
8780 1
< 0.1%
8706 1
< 0.1%
8645 1
< 0.1%
8550 1
< 0.1%
8500 1
< 0.1%
8430 1
< 0.1%
8200 1
< 0.1%
7850 1
< 0.1%
7845 1
< 0.1%

service_charge
Real number (ℝ)

ZEROS 

Distinct732
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.61397
Minimum0
Maximum6045
Zeros2265
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-04-15T22:22:01.041860image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q195
median135
Q3187
95-th percentile300
Maximum6045
Range6045
Interquartile range (IQR)92

Descriptive statistics

Standard deviation86.084007
Coefficient of variation (CV)0.57537412
Kurtosis136.89001
Mean149.61397
Median Absolute Deviation (MAD)45
Skewness4.1294227
Sum27846601
Variance7410.4562
MonotonicityNot monotonic
2024-04-15T22:22:01.179084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 9969
 
5.4%
100 9034
 
4.9%
200 7663
 
4.1%
120 7574
 
4.1%
130 5415
 
2.9%
140 5148
 
2.8%
80 4902
 
2.6%
180 4676
 
2.5%
90 4469
 
2.4%
110 4450
 
2.4%
Other values (722) 122823
66.0%
ValueCountFrequency (%)
0 2265
1.2%
1 18
 
< 0.1%
2 14
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
10 6
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 2
 
< 0.1%
15 14
 
< 0.1%
ValueCountFrequency (%)
6045 1
< 0.1%
2150 1
< 0.1%
1977 1
< 0.1%
1920 1
< 0.1%
1837 1
< 0.1%
1800 1
< 0.1%
1740 1
< 0.1%
1700 1
< 0.1%
1580 1
< 0.1%
1540 1
< 0.1%

immoscout_id
Real number (ℝ)

UNIQUE 

Distinct186123
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.065018 × 108
Minimum28871743
Maximum1.1571166 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-04-15T22:22:01.320362image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum28871743
5-th percentile76003942
Q11.0645841 × 108
median1.1106221 × 108
Q31.1374517 × 108
95-th percentile1.155979 × 108
Maximum1.1571166 × 108
Range86839917
Interquartile range (IQR)7286763.5

Descriptive statistics

Standard deviation13003773
Coefficient of variation (CV)0.12209909
Kurtosis7.3399625
Mean1.065018 × 108
Median Absolute Deviation (MAD)3686983
Skewness-2.6246264
Sum1.9822434 × 1013
Variance1.6909811 × 1014
MonotonicityNot monotonic
2024-04-15T22:22:01.457572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96107057 1
 
< 0.1%
106362091 1
 
< 0.1%
112873334 1
 
< 0.1%
113698989 1
 
< 0.1%
110476726 1
 
< 0.1%
111619968 1
 
< 0.1%
106844375 1
 
< 0.1%
111072780 1
 
< 0.1%
111418434 1
 
< 0.1%
107744065 1
 
< 0.1%
Other values (186113) 186113
> 99.9%
ValueCountFrequency (%)
28871743 1
< 0.1%
29301391 1
< 0.1%
29301750 1
< 0.1%
29370795 1
< 0.1%
29506747 1
< 0.1%
29707618 1
< 0.1%
29718404 1
< 0.1%
29718866 1
< 0.1%
29718867 1
< 0.1%
29718995 1
< 0.1%
ValueCountFrequency (%)
115711660 1
< 0.1%
115711556 1
< 0.1%
115711546 1
< 0.1%
115711544 1
< 0.1%
115711540 1
< 0.1%
115711539 1
< 0.1%
115711538 1
< 0.1%
115711536 1
< 0.1%
115711535 1
< 0.1%
115711534 1
< 0.1%

Interactions

2024-04-15T22:21:53.921563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:45.312536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.368267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.367117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:48.461015image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:49.596931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:50.687665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.817733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.831393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:54.046283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:45.428575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.478219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.478782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:48.578861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:49.716192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:50.797243image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.926161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.942854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:54.175735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:45.539352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.580451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.595778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:48.692306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:49.840471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.027142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.032752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:53.053288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:54.300380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:45.660603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.688720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.736502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:48.809668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:49.964240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.141088image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.148661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:53.194006image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:54.433126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:45.783368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.803130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.856900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:48.928309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:50.090491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.258733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.265359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:53.320266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:54.561852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:45.902419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.915946image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.977113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:49.054083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:50.211231image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.371424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.377158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:53.441124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:54.682112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.011216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.017165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:48.087421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:49.187417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:50.322207image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.474880image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.484321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:53.552371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:54.805302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.119459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.128865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:48.210589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:49.312063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:50.432850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.577539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.587792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:53.660116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:54.947120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:46.256734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:47.258380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:48.350292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:49.455055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:50.573973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:51.709564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:52.720457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-15T22:21:53.792011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-04-15T22:21:55.132526image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-15T22:21:55.550862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

bundeslandcitydistrictstreetzip_codehas_kitchenbalconyliftgardenfloorflat_typetelekom_uploadspeedfiring_typeheating_typenumber_of_roomssquare_meterbase_renttotal_rentservice_chargeimmoscout_id
0Nordrhein_WestfalenDortmundSchürenSch&uuml;ruferstra&szlig;e44269FalseFalseFalseTrue1.0ground_floor10.0oilcentral_heating486595840245.096107057
1SachsenDresdenÄußere_Neustadt_AntonstadtTurnerweg1097FalseTrueTrueFalse3.0apartment2.4NaNfloor_heating3849651300255.0113147523
2BremenBremenNeu_SchwachhausenHermann-Henrich-Meier-Allee28213FalseTrueFalseFalse1.0apartmentNaNgasself_contained_central_heating385765903138.0114751222
3SachsenMittelsachsen_KreisFreibergAm Bahnhof9599FalseFalseFalseTrue1.0NaN2.4NaNself_contained_central_heating26231038070.0114391930
5Baden_WürttembergEmmendingen_KreisDenzlingenno_information79211TrueFalseFalseFalse2.0roof_storey40.0oiloil_heating253580690110.0106416361
6SachsenChemnitzSonnenbergHofer Stra&szlig;e9130FalseTrueFalseFalse3.0apartment40.0gasNaN24021930788.0112923517
7SachsenMittelsachsen_KreisFrankenberg/Sachsenno_information9669FalseFalseFalseTrue1.0NaN2.4gascentral_heating380400555155.0109842225
9Nordrhein_WestfalenHammMitteno_information59065FalseFalseFalseFalse4.0apartment40.0oilcentral_heating41239501150200.0101730329
10Nordrhein_WestfalenDortmundKirchhördeAm Dimberg44229FalseTrueTrueFalse0.0ground_floor2.4gasgas_heating3879731321215.092798563
11ThüringenWeimarSchöndorfBirkenhof99427FalseTrueFalseFalse4.0apartment2.4district_heatingdistrict_heating13722030080.0106896167
bundeslandcitydistrictstreetzip_codehas_kitchenbalconyliftgardenfloorflat_typetelekom_uploadspeedfiring_typeheating_typenumber_of_roomssquare_meterbase_renttotal_rentservice_chargeimmoscout_id
228321Nordrhein_WestfalenNeuss_Rhein_KreisNeussFurther Str.41462FalseFalseFalseFalse4.0apartment40.0natural_gas_lightself_contained_central_heating398740920180.0113557363
228322Sachsen_AnhaltMagdeburgHopfengartenGustav-Ricker-Str.39120FalseTrueFalseFalse1.0otherNaNheat_supplycentral_heating255380515135.0113358959
228323BayernMünchenMaxvorstadtno_information80799TrueTrueFalseFalse0.0ground_floor10.0NaNdistrict_heating26517801980100.0104772937
228324HessenFrankfurt_am_MainPreungesheimGundelandst.60435TrueTrueTrueTrue2.0apartment2.4district_heatingdistrict_heating39012551480112.0106995489
228325Sachsen_AnhaltMagdeburgCracauThomas-Mann-Str.39114FalseTrueFalseFalse2.0apartment40.0gas:electricitycentral_heating35730342598.0110721511
228326SachsenZwickauNordvorstadtM&uuml;hlpfortstra&szlig;e8058TrueFalseFalseFalse3.0maisonette40.0NaNNaN260300440140.0111857041
228327SachsenChemnitzKappelNeefestra&szlig;e9119FalseTrueFalseTrue1.0apartment40.0gascentral_heating255248368120.091110231
228328Nordrhein_WestfalenEssenHorstno_information45279FalseFalseFalseFalse3.0roof_storey2.4gasgas_heating38559067080.0115526313
228330HessenBergstraße_KreisViernheimno_information68519TrueTrueFalseFalse1.0apartmentNaNgasgas_heating41159301150220.096981497
228331HessenLimburg_Weilburg_KreisLimburg_an_der_LahnEmsbachstrasse65552FalseTrueFalseTrue1.0apartment40.0gascentral_heating495650930220.066924271